[shardformer/sequence parallel] support gpt2 seq parallel with pp/dp/tp (#4460)

* support gpt2 seq parallel with pp/dp/tp

* fix a bug when waiting for stream done

* delete unused gpt2_seq file

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -235,6 +235,10 @@ class HybridParallelPlugin(PipelinePluginBase):
         assert dist.get_world_size() % (
             tp_size * pp_size
         ) == 0, f'world size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}'
+
+        if enable_sequence_parallelism:
+            assert tp_size > 1, 'Sequence parallelism must be enabled when using tensor parallelism'
+
         # TODO(ver217): support zero
         assert zero_stage == 0, 'zero is not support yet'
         self.tp_size = tp_size

colossalai/shardformer/layer/_operation.py

@@ -239,6 +239,7 @@ class _LinearWithGatherForwardReduceScatterBackward(torch.autograd.Function):
                 output = torch.empty(input_.shape, dtype=input_.dtype, device=input_.device).contiguous()
 
             torch.cuda.current_stream().wait_stream(calculate_stream)
+            gather_handle.wait()
 
             reducescatter_handle = dist.reduce_scatter(output, input_list, group=process_group, async_op=True)
             with torch.cuda.stream(calculate_stream):
@@ -249,6 +250,7 @@ class _LinearWithGatherForwardReduceScatterBackward(torch.autograd.Function):
                 grad_weight = grad_output.t().matmul(input_parallel)
 
             torch.cuda.current_stream().wait_stream(calculate_stream)
+            reducescatter_handle.wait()
 
         return output, grad_weight, grad_bias, None, None, None, None
 

colossalai/shardformer/modeling/gpt2_seq.py

-# this code is modified from transformers.models.gpt2.modeling_gpt2
-# https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/gpt2/modeling_gpt2.py#L670
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.distributed as dist
-from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
-from transformers.utils import logging
-
-from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
-from colossalai.shardformer.shard import ShardConfig
-
-logger = logging.get_logger(__name__)
-
-
-# TODO: put all contents in `gpt2.py` and make it compatible with pipeline
-def gpt2_sequence_parallel_forward_fn(shard_config: ShardConfig):
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (output_hidden_states
-                                if output_hidden_states is not None else self.config.output_hidden_states)
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-            batch_size = input_ids.shape[0]
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size = inputs_embeds.shape[0]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if token_type_ids is not None:
-            token_type_ids = token_type_ids.view(-1, input_shape[-1])
-        if position_ids is not None:
-            position_ids = position_ids.view(-1, input_shape[-1])
-
-        if past_key_values is None:
-            past_length = 0
-            past_key_values = tuple([None] * len(self.h))
-        else:
-            past_length = past_key_values[0][0].size(-2)
-        if position_ids is None:
-            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
-
-        # GPT2Attention mask.
-        if attention_mask is not None:
-            if batch_size <= 0:
-                raise ValueError("batch_size has to be defined and > 0")
-            attention_mask = attention_mask.view(batch_size, -1)
-            # We create a 3D attention mask from a 2D tensor mask.
-            # Sizes are [batch_size, 1, 1, to_seq_length]
-            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-            # this attention mask is more simple than the triangular masking of causal attention
-            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-            attention_mask = attention_mask[:, None, None, :]
-
-            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-            # masked positions, this operation will create a tensor which is 0.0 for
-            # positions we want to attend and the dtype's smallest value for masked positions.
-            # Since we are adding it to the raw scores before the softmax, this is
-            # effectively the same as removing these entirely.
-            attention_mask = attention_mask.to(dtype=self.dtype)    # fp16 compatibility
-            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.add_cross_attention and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-            encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # head_mask has shape n_layer x batch x n_heads x N x N
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-        position_embeds = self.wpe(position_ids)
-        hidden_states = inputs_embeds + position_embeds
-
-        if token_type_ids is not None:
-            token_type_embeds = self.wte(token_type_ids)
-            hidden_states = hidden_states + token_type_embeds
-
-        hidden_states = self.drop(hidden_states)
-
-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
-                use_cache = False
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-        all_hidden_states = () if output_hidden_states else None
-
-        # split the input tensor along sequence dimension
-        # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
-        hidden_states = split_forward_gather_backward(hidden_states,
-                                                      dim=1,
-                                                      process_group=shard_config.tensor_parallel_process_group)
-
-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
-            # Model parallel
-            if self.model_parallel:
-                torch.cuda.set_device(hidden_states.device)
-                # Ensure layer_past is on same device as hidden_states (might not be correct)
-                if layer_past is not None:
-                    layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
-                # Ensure that attention_mask is always on the same device as hidden_states
-                if attention_mask is not None:
-                    attention_mask = attention_mask.to(hidden_states.device)
-                if isinstance(head_mask, torch.Tensor):
-                    head_mask = head_mask.to(hidden_states.device)
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, use_cache, output_attentions)
-
-                    return custom_forward
-
-                outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    None,
-                    attention_mask,
-                    head_mask[i],
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                outputs = block(
-                    hidden_states,
-                    layer_past=layer_past,
-                    attention_mask=attention_mask,
-                    head_mask=head_mask[i],
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_attention_mask,
-                    use_cache=use_cache,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
-                if self.config.add_cross_attention:
-                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
-
-            # Model Parallel: If it's the last layer for that device, put things on the next device
-            if self.model_parallel:
-                for k, v in self.device_map.items():
-                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
-                        hidden_states = hidden_states.to("cuda:" + str(k + 1))
-
-        # When sequence parallelism done, gather the output tensor in forward and split it in backward
-        hidden_states = gather_forward_split_backward(hidden_states,
-                                                      dim=1,
-                                                      process_group=shard_config.tensor_parallel_process_group)
-
-        hidden_states = self.ln_f(hidden_states)
-        hidden_states = hidden_states.view(output_shape)
-        # Add last hidden state
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
-                if v is not None)
-
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=presents,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-    return forward

colossalai/shardformer/policies/gpt2.py

@@ -6,8 +6,7 @@ from torch import Tensor, nn
 import colossalai.shardformer.layer as col_nn
 
 from .._utils import getattr_, setattr_
-from ..modeling.gpt2 import GPT2PipelineForwards, get_gpt2_flash_attention_forward
-from ..modeling.gpt2_seq import gpt2_sequence_parallel_forward_fn
+from ..modeling.gpt2 import GPT2PipelineForwards, get_gpt2_flash_attention_forward, gpt2_sequence_parallel_forward_fn
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
 __all__ = [
@@ -50,8 +49,6 @@ class GPT2Policy(Policy):
                     target_module=col_nn.DropoutForParallelInput,
                 ),
             ])
-            if self.shard_config.enable_sequence_parallelism:
-                policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)}
 
             policy[GPT2Block] = ModulePolicyDescription(attribute_replacement={
                 "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
@@ -126,6 +123,7 @@ class GPT2Policy(Policy):
             })
 
         if self.shard_config.enable_sequence_parallelism:
+            policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)}
             suffix_list = ["attn.c_attn", "attn.c_proj", "mlp.c_fc", "mlp.c_proj"]
             self.append_seq_parallel_to_policy(suffix_list=suffix_list, module_policy_description=policy[GPT2Block])
 
@@ -169,7 +167,13 @@ class GPT2Policy(Policy):
 
         layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
         stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
-        method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
+        method_replacement = {
+            'forward':
+                partial(new_forward,
+                        stage_manager=stage_manager,
+                        stage_index=stage_index,
+                        shard_config=self.shard_config)
+        }
         self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
 
 

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -105,10 +105,18 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
     'enable_all_optimization': True,
     'use_lazy_init': False,
     'precision': 'fp32',
+}, {
+    'tp_size': 2,
+    'pp_size': 2,
+    'num_microbatches': 4,
+    'enable_all_optimization': True,
+    'use_lazy_init': True,
+    'enable_sequence_parallelism': True,
+    'precision': 'fp32',
 }, {
     'tp_size': 4,
     'pp_size': 1,
-    'enable_all_optimization': False,
+    'enable_all_optimization': True,
     'use_lazy_init': True,
     'enable_sequence_parallelism': True,
     'precision': 'fp32',